Evaluating the Influence of Chatbots and AI Assistants on Medical Communication and Patient Trust

Authors

DOI:

https://doi.org/10.56294/mw2024495

Keywords:

Chatbots, AI Assistants, Medical Communication, Patient Trust, Healthcare Technology

Abstract

Using robots and AI helpers in healthcare is changing how patients communicate with medical services. This could be a good way to improve communication, get patients more involved, and maybe even build trust in healthcare delivery.  This research looks at how these digital tools affect how doctors and patients talk to each other and trust each other.  The quick spread of AI-powered systems in healthcare settings has led to talks about how well they help build real relationships between healthcare workers and patients and how they can make healthcare more accessible and efficient.  The main goal of the study is to look at how patients and healthcare workers feel about AI being used in hospital settings, focussing on how much patients trust and are satisfied with the technology.  A mixed-method approach was used, with people from a wide range of groups taking part in both quantitative polls and qualitative conversations.  People who used AI-based apps and helpers in healthcare settings, such as to check for symptoms, make appointments, and send follow-up messages, were asked to provide data.  The study looks into how these tools affect what patients expect, how happy they are with conversation, and how much they believe AI systems that give them medical advice.  The results show that patients have mostly good experiences with AI helpers, especially when it comes to things like ease of use, quick answers, and availability 24 hours a day, seven days a week.  Concerns about how artificial intelligence would not be able to provide humane treatment and the requirement of human supervision in medical decision-making surfaced, nevertheless.  The research claims that in certain cases artificial intelligence might increase trust and connection; yet, it should be utilised cautiously and that patient care still depends much on human contact.  Future research should concentrate on making AI-driven systems in healthcare more accurate, sympathetic, and transparent if we are to fully maximise them.

References

Dave, M.; Patel, N. Artificial intelligence in healthcare and education. Br. Dent. J. 2023, 234, 761–764.

Brambilla, A.; Sun, T.-Z.; Elshazly, W.; Ghazy, A.; Barach, P.; Lindahl, G.; Capolongo, S. Flexibility during the COVID-19 Pandemic Response: Healthcare Facility Assessment Tools for Resilient Evaluation. Int. J. Environ. Res. Public Health 2021, 18, 11478.

Prakash, S.; Balaji, J.N.; Joshi, A.; Surapaneni, K.M. Ethical Conundrums in the Application of Artificial Intelligence (AI) in Healthcare-A Scoping Review of Reviews. J. Pers. Med. 2022, 12, 1914.

Cacciamani, G.E.; Chu, T.N.; Sanford, D.I.; Abreu, A.; Duddalwar, V.; Oberai, A.; Kuo, C.-C.J.; Liu, X.; Denniston, A.K.; Vasey, B.; et al. PRISMA AI reporting guidelines for systematic reviews and meta-analyses on AI in healthcare. Nat. Med. 2023, 29, 14–15.

Pisapia, A.; Banfi, G.; Tomaiuolo, R. The novelties of the regulation on health technology assessment, a key achievement for the European union health policies. Clin. Chem. Lab. Med. CCLM 2022, 60, 1160–1163.

Wang, C.; Zhang, J.; Lassi, N.; Zhang, X. Privacy Protection in Using Artificial Intelligence for Healthcare: Chinese Regulation in Comparative Perspective. Healthcare 2022, 10, 1878.

Townsend, B.A.; Sihlahla, I.; Naidoo, M.; Naidoo, S.; Donnelly, D.-L.; Thaldar, D.W. Mapping the regulatory landscape of AI in healthcare in Africa. Front. Pharmacol. 2023, 14, 1214422.

Marengo, A.; Pagano, A. Investigating the Factors Influencing the Adoption of Blockchain Technology across Different Countries and Industries: A Systematic Literature Review. Electronics 2023, 12, 3006.

Moldt, J.-A.; Festl-Wietek, T.; Madany Mamlouk, A.; Nieselt, K.; Fuhl, W.; Herrmann-Werner, A. Chatbots for future docs: Exploring medical students’ attitudes and knowledge towards artificial intelligence and medical chatbots. Med. Educ. Online 2023, 28, 2182659.

Bartels, R.; Dudink, J.; Haitjema, S.; Oberski, D.; van ‘t Veen, A. A Perspective on a Quality Management System for AI/ML-Based Clinical Decision Support in Hospital Care. Front. Digit. Health 2022, 4, 942588.

Feng, J.; Phillips, R.V.; Malenica, I.; Bishara, A.; Hubbard, A.E.; Celi, L.A.; Pirracchio, R. Clinical artificial intelligence quality improvement: Towards continual monitoring and updating of AI algorithms in healthcare. npj Digit. Med. 2022, 5, 66.

Kiran Kumara, Vivek Kant Jogi. (2016). Design of Road Tracing System for Computer Vision. Advance Physics Letter, 3(2), 43-45

Lorenzon, M.; Spina, E.; Franco, F.T.D.; Giovannini, I.; Vita, S.D.; Zabotti, A. Salivary Gland Ultrasound in Primary Sjögren’s Syndrome: Current and Future Perspectives. Open Access Rheumatol. Res. Rev. 2022, 14, 147–160.

Hogg, H.D.J.; Al-Zubaidy, M.; Talks, J.; Denniston, A.K.; Kelly, C.J.; Malawana, J.; Papoutsi, C.; Teare, M.D.; Keane, P.A.; Beyer, F.R.; et al. Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence. J. Med. Internet Res. 2023, 25, 39742.

Miller, G.J. Stakeholder roles in artificial intelligence projects. Proj. Leadersh. Soc. 2022, 3, 100068.

Kordi, M.; Dehghan, M.J.; Shayesteh, A.A.; Azizi, A. The impact of artificial intelligence algorithms on management of patients with irritable bowel syndrome: A systematic review. Inform. Med. Unlocked 2022, 29, 100891.

Alcocer Alkureishi, M.; Lenti, G.; Choo, Z.-Y.; Castaneda, J.; Weyer, G.; Oyler, J.; Lee, W.W. Teaching Telemedicine: The Next Frontier for Medical Educators. JMIR Med. Educ. 2021, 7, e29099.

Chee, M.L.; Ong, M.E.H.; Siddiqui, F.J.; Zhang, Z.; Lim, S.L.; Ho, A.F.W.; Liu, N. Artificial intelligence applications for COVID-19 in intensive care and emergency settings: A systematic review. Int. J. Environ. Res. Public Health 2021, 18, 4749.

Xu, Z.; Su, C.; Xiao, Y.; Wang, F. Artificial intelligence for COVID-19: Battling the pandemic with computational intelligence. Intell. Med. 2022, 2, 13–29.

Downloads

Published

2024-12-31

How to Cite

1.
Chandana P, Samrat B, Suri S, Majumder R, Patil S, Ku.Sahu P. Evaluating the Influence of Chatbots and AI Assistants on Medical Communication and Patient Trust. Seminars in Medical Writing and Education [Internet]. 2024 Dec. 31 [cited 2025 Jul. 5];3:495. Available from: https://mw.ageditor.ar/index.php/mw/article/view/495